title: Meta-analysis of transcriptome reveals let-7b as an unfavorable prognostic biomarker and predicts molecular and clinical subclasses in high-grade serous ovarian carcinoma.
abstract: High-grade serous ovarian carcinoma (HG-SOC) is a heterogeneous, poorly classified, lethal disease that frequently exhibits altered expressions of microRNAs. Let-7 family members are often reported as tumor suppressors; nonetheless, clinicopathological functions and prognostic values of individual let-7 family members have not been addressed in HG-SOC. In our work, we performed an integrative study to investigate the potential roles, clinicopathological functions and prognostic values of let-7 miRNA family in HG-SOC. Using microarray and clinical data of 1,170 HG-SOC patients, we developed novel survival prediction and system biology methods to analyze prognostic values and functional associations of let-7 miRNAs with global transcriptome and clinicopathological factors. We demonstrated that individual let-7 members exhibit diverse evolutionary history and distinct regulatory characteristics. Statistical tests and network analysis suggest that let-7b could act as a global synergistic interactor and master regulator controlling hundreds of protein-coding genes. The elevated expression of let-7b is associated with poor survival rates, which suggests an unfavorable role of let-7b in treatment response for HG-SOC patients. A novel let-7b-defined 36-gene prognostic survival signature outperforms many clinicopathological parameters, and stratifies HG-SOC patients into three high-confidence, reproducible, clinical subclasses: low-, intermediate- and high-risk, with 5-year overall survival rates of 56-71%, 12-29% and 0-10%, respectively. Furthermore, the high-risk and low-risk subclasses exhibit strong mesenchymal and proliferative tumor phenotypes concordant with resistance and sensitivity to primary chemotherapy. Our results have led to identification of promising prognostic markers of HG-SOC, which could provide a rationale for genetic-based stratification of patients and optimization of treatment regimes.
authors.affiliation: Bioinformatics Institute, A*STAR, Singapore.
authors.forename: Zhiqun
authors.initials: Z
authors.lastname: Tang
authors.forename: Ghim Siong
authors.initials: GS
authors.lastname: Ow
authors.forename: Jean Paul
authors.initials: JP
authors.lastname: Thiery
authors.forename: Anna V
authors.initials: AV
authors.lastname: Ivshina
authors.forename: Vladimir A
authors.initials: VA
authors.lastname: Kuznetsov
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000235:genetics*; D018284:Cystadenocarcinoma, Serous / Q000145:classification* / Q000235:genetics / Q000401:mortality / Q000473:pathology; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D008875:Middle Aged; D060787:Neoplasm Grading; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D020411:Oligonucleotide Array Sequence Analysis; D010051:Ovarian Neoplasms / Q000145:classification* / Q000235:genetics / Q000401:mortality / Q000473:pathology; D011379:Prognosis; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs; C487931:mirnlet7 microRNA, human
keywords: cancer biomarker; high-grade serous ovarian carcinoma; microRNA let-7; survival prognosis; tumor classification